基于改进狼群算法的水电站厂内优化运行研究  

Research on Inner-plant Optimal Operation of Hydropower Station Based on Improved Wolf Swarm Algorithm

作　　者：李励 赖喜德 陈小明 LI Li;LAI Xi-de;CHEN Xiao-ming(School of Energy and Power Engineering,Xihua University,Chengdu 610039,China)

机构地区：[1]西华大学能源与动力工程学院

出　　处：《水电能源科学》2019年第6期164-168,共5页Water Resources and Power

基　　金：四川省科技计划项目(2017GZ0053,2017NZ0031);国家自然科学基金项目(51379179);流体及动力机械教育部重点实验室项目

摘　　要：针对水电站厂内运行中机组负荷分配问题的高维度、多约束的特点,提出了一种基于差分进化算法的自适应狼群算法,结合水电站厂内优化运行的优化目标和约束条件,以龙潭水电站日常运行数据为基础,采用该算法进行求解,并与遗传算法和原狼群算法计算结果进行比较。结果表明,该算法在计算耗时和收敛精度上均较遗传算法和原狼群算法有一定的改善,能满足电站实时性和精度要求,具有良好的全局搜索能力,在计算高维多机组负荷分配问题时能快速有效摆脱局部最优情况趋向全局最优解。Aiming at the characteristics of high dimensional and multi constraints in the load distribution of inner plant operation in hydropower station,an improved wolf swarm algorithm based differential evolution was proposed.Based on the daily operation data of Longtan Hydropower Station,the algorithm was used to solve the problem by combining the optimization objectives and constraints of the optimal operation of hydropower station.The results were compared with those of the genetic algorithm and wolf swarm algorithm.The results show that the improved algorithm has better improvement in computation time and convergence accuracy than genetic algorithm and wolf swarm algorithm,which can meet the real time and accuracy requirements of the hydropower station.The algorithm has good global search ability and calculates the load distribution problem of high dimensional multi units.It can quickly and effectively get rid of the local optimal situation and tend to the global optimal solution.

关 键 词：水电站 厂内优化运行 狼群算法 自适应 差分进化 

分 类 号：TV74[水利工程—水利水电工程]